标题： 基于非平稳高斯过程的异常检测与去除 显示英文标题

标题： Anomaly Detection and Removal Using Non-Stationary Gaussian Processes

摘要： 本文提出了一种新颖的高斯过程方法来处理时间序列数据中的故障去除问题。 故障去除不会删除有故障的信号数据，而是从数据中修正故障。我们假设任何时候只发生一个故障，并用两个独立的非参数化高斯过程模型分别对物理现象和故障进行建模。 为了便于故障去除，我们引入了马尔可夫区域链接核来处理非平稳高斯过程。该核函数分段平稳，但保证由其生成的函数及其导数（如有需要）在所有位置连续。 我们将此核函数应用于修正有故障传感器数据中的漂移和偏置错误，以及恢复被EOG伪影污染的EEG信号。 显示英文摘要

摘要： This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process models for both the physical phenomenon and the fault. In order to facilitate fault removal we introduce the Markov Region Link kernel for handling non-stationary Gaussian processes. This kernel is piece-wise stationary but guarantees that functions generated by it and their derivatives (when required) are everywhere continuous. We apply this kernel to the removal of drift and bias errors in faulty sensor data and also to the recovery of EOG artifact corrupted EEG signals.